# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Abstraction of multi-task model."""
from typing import Text, Dict

import tensorflow as tf


class MultiTaskBaseModel(tf.Module):
    """Base class that holds multi-task model computation."""

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self._sub_tasks = self._instantiate_sub_tasks()

    def _instantiate_sub_tasks(self) -> Dict[Text, tf.keras.Model]:
        """Abstract function that sets up the computation for each sub-task.

    Returns:
      A map from task name (as string) to a tf.keras.Model object that
        represents the sub-task in the multi-task pool.
    """
        raise NotImplementedError(
            "_instantiate_sub_task_models() is not implemented.")

    @property
    def sub_tasks(self):
        """Fetch a map of task name (string) to task model (tf.keras.Model)."""
        return self._sub_tasks

    def initialize(self):
        """Optional function that loads a pre-train checkpoint."""
        return

    def build(self):
        """Builds the networks for tasks to make sure variables are created."""
        # Try to build all sub tasks.
        for task_model in self._sub_tasks.values():
            # Assumes all the tf.Module models are built because we don't have any
            # way to check them.
            if isinstance(task_model, tf.keras.Model) and not task_model.built:
                _ = task_model(task_model.inputs)
